It’s an important issue when planning clinical studies of CAM.

Researchers from the David Geffen School of Medicine at the University of California, Los Angeles address this issue for a study of acupuncture in the relief of post-chemotherapy fatigue in breast cancer patients.

First, the details.

• A PubMed search identified 3 uncontrolled studies (no comparison group) reporting the effect of acupuncture in relieving fatigue.
• A separate search identified 5 randomized controlled trials (treatment vs standard care or placebo).
• Each used a wait-list control of breast cancer patients receiving standard care and reported data on fatigue.
• The authors used the data to estimate the number of patients for a study of acupuncture in the relief of cancer-related fatigue.

And, the results.

• The authors calculated that an adequately powered study with 2 treatment groups would require at least 101 subjects (52 per treatment group) to detect a strong effect for acupuncture and 235 (118 per arm) if a moderate effect is assumed.
• Strong effect: greater than 80% chance of detecting a clinically significant effect when one exists).
• Moderate effect: 50% chance of detecting a clinically significant effect when one exists.

The bottom line?
Read the article for more information on the 4-step process of evidence-based effect size estimation.

• Step 1: draw upon published literature to specify a range of assumptions about the amount of change in the treatment group and the amount of change in the control group.
• Step 2: make a plausible assumption about the amount of change in the treatment group relative to control based upon evidence gathered in step 1.
• Step 3: carry out a power analysis to estimate sample size.
• Step 4: refine assumptions about effect size.

I’ll add step 5: make friends with a statistician.

1/18/09 22:15 JR